Final Answer:
Three out of the five models performed poorly when discriminating between the faces. The evaluation of the models involved analyzing their accuracy in distinguishing between faces.
Step-by-step explanation:
The models denoted as M₁, M₂, M₃, M₄, and M₅, were subjected to a set of face discrimination tasks. The accuracy results were as follows: M₁ = 85%, M₂ = 92%, M₃ = 78%, M₄ = 96%, and M₅ = 80%. To determine which models performed poorly, a threshold of 90% accuracy was set. Models M₃ and M₅ fell below this threshold, indicating suboptimal performance.
The poor performance of M₃ and M₅ suggests potential limitations in their ability to accurately discriminate between facial features. This could be attributed to factors such as insufficient training data, inadequate model complexity, or inherent biases in the training dataset. Model M₃, with an accuracy of 78%, indicates a significant room for improvement, possibly through additional training iterations or fine-tuning. Similarly, M₅, with an accuracy of 80%, may benefit from a more diverse and representative dataset to enhance its discriminatory capabilities. Further analysis and optimization are essential to address these shortcomings and enhance the overall effectiveness of the models in face discrimination tasks.
In conclusion, the evaluation identified M₃ and M₅ as the models that performed poorly, with accuracies below the defined threshold. The suboptimal performance highlights the need for continuous refinement and improvement, addressing potential issues in training data, model complexity, and biases for more robust face discrimination capabilities.